PROJECT SUMMARY Continued trends of alcohol and substance exposed pregnancy (ASEP) indicate a great need for higher quality ASEP-reduction programs, particularly those that address ASEP health disparities within at-risk populations and communities, such as American Indian (AI) women. These programs do not account for the broad constellation of factors pertinent to ASEP, in particular, the role of intimate partner violence (IPV), which forms a syndemic association with two other ASEP indicators (alcohol and substance use and unplanned pregnancy). System dynamics methods are effective strategies for understanding of how ASEP and this syndemic are nested within a broader system of interpersonal, intrapersonal, and institutional factors. This method is especially beneficial for addressing the current ASEP-related health disparities within AI communities. Community-based system dynamic models allow practitioners and policymakers to determine the best system areas for implementing policies and programs that will produce the biggest changes in ASEP. The current proposal uses community- based approaches to develop ASEP system models for AI women within two communities: a small metro and a neighboring reservation. These models allow for a researcher-community partnership to discover important system leverage points for ASEP intervention (reducing ASEP within pregnant women) and ASEP prevention (focusing on the cyclic relationship between IPV and alcohol and substance use). The goals of this project are to build and simulate system dynamic models that that represent the ASEP system in partnership with our highly collaborative community-researcher team. We will calibrate and validate these models utilizing a variety of community data sources, and then distinguish the most effective areas to target for reducing ASEP and ASEP predictors that a) generalize across communities and substance legality, and b) may be uniquely effective within specific communities or for specific substances. This work will be complemented by individual-level analyses which can estimate the strength of high-priority leverage points on individual ASEP and ASEP risk. The proposed research is significant as it accounts for the often-ignored underlying matrix of contributors which maintain community levels of ASEP and ASEP health disparities, and provides clear recommendations for high-impact methods to reduce ASEP within communities at need. This project is innovative due to the integration of system simulation and community-based approaches to address complexity, of this issue, and the integration of descriptive and predictive analyses to provide distinct empirically-based solutions to address these issues within a translational framework. The strong interdisciplinary team of researchers with unique, but complementary areas of expertise and the close working partnership between researchers and the communities of interest are together a powerful collaborative to facilitate project success and meaningful contributions to community health. Findings from this study provide critical information about highly effective ways of reducing ASEP and ASEP health disparities, as well as a clear mechanism for developing a strategic blueprint for systematic change within AI communities.